Triple
T1452479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Army Group Don |
E31321
|
entity |
| Predicate | militaryBranch |
P253
|
FINISHED |
| Object | Heer |
E9485
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Heer | Statement: [Army Group Don, militaryBranch, Heer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Heer Context triple: [Army Group Don, militaryBranch, Heer]
-
A.
Heer
chosen
The Heer was the land-based component of Nazi Germany’s armed forces, serving as its primary army during World War II.
-
B.
Hein
Hein is a Dutch surname most notably borne by Piet Hein, a renowned 17th-century naval officer and folk hero of the Dutch Republic.
-
C.
Ritter
Ritter is the surname of Thelma Ritter, the acclaimed American character actress known for her sharp-tongued, working-class roles in mid-20th-century Hollywood films.
-
D.
König
König is a German-language surname borne by numerous individuals, including notable figures in fields such as religion, science, and the arts.
-
E.
Kœnig
Kœnig is a French surname most notably associated with figures such as General Marie-Pierre Kœnig, a prominent military leader during World War II.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a499171a28819085b993a3ac78e363 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c57d34cc8190801b769d9d9b2e2e |
completed | March 1, 2026, 11:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad08c6f7c881908d1ef9f7897895a6 |
completed | March 8, 2026, 5:27 a.m. |
Created at: March 1, 2026, 8 p.m.